LiFR-Law is a corpus of Czech legal and administrative texts with measured reading comprehension and a subjective expert annotation of diverse textual properties based on the Hamburg Comprehensibility Concept (Langer, Schulz von Thun, Tausch, 1974). It has been built as a pilot data set to explore the Linguistic Factors of Readability (hence the LiFR acronym) in Czech administrative and legal texts, modeling their correlation with actually observed reading comprehension. The corpus is comprised of 18 documents in total; that is, six different texts from the legal/administration domain, each in three versions: the original and two paraphrases. Each such document triple shares one reading-comprehension test administered to at least thirty readers of random gender, educational background, and age. The data set also captures basic demographic information about each reader, their familiarity with the topic, and their subjective assessment of the stylistic properties of the given document, roughly corresponding to the key text properties identified by the Hamburg Comprehensibility Concept.
LiFR-Law is a corpus of Czech legal and administrative texts with measured reading comprehension and a subjective expert annotation of diverse textual properties based on the Hamburg Comprehensibility Concept (Langer, Schulz von Thun, Tausch, 1974). It has been built as a pilot data set to explore the Linguistic Factors of Readability (hence the LiFR acronym) in Czech administrative and legal texts, modeling their correlation with actually observed reading comprehension. The corpus is comprised of 18 documents in total; that is, six different texts from the legal/administration domain, each in three versions: the original and two paraphrases. Each such document triple shares one reading-comprehension test administered to at least thirty readers of random gender, educational background, and age. The data set also captures basic demographic information about each reader, their familiarity with the topic, and their subjective assessment of the stylistic properties of the given document, roughly corresponding to the key text properties identified by the Hamburg Comprehensibility Concept.
Changes to the previous version and helpful comments
• File names of the comprehension test results (self-explanatory)
• Corrected one erroneous automatic evaluation rule in the multiple-choice evaluation (zahradnici_3,
TRUE and FALSE had been swapped)
• Evaluation protocols for both question types added into Folder lifr_formr_study_design
• Data has been cleaned: empty responses to multiple-choice questions were re-inserted. Now, all surveys
are considered complete that have reader’s subjective text evaluation complete (these were placed at
the very end of each survey).
• Only complete surveys (all 7 content questions answered) are represented. We dropped the replies of
six users who did not complete their surveys.
• A few missing responses to open questions have been detected and re-inserted.
• The demographic data contain all respondents who filled in the informed consent and the demographic
details, with respondents who did not complete any test survey (but provided their demographic
details) in a separate file. All other data have been cleaned to contain only responses by the regular
respondents (at least one completed survey).
Corpus of Czech educational texts for readability studies, with paraphrases, measured reading comprehension, and a multi-annotator subjective rating of selected text features based on the Hamburg Comprehensibility Concept
Corpus of Czech educational texts for readability studies, with paraphrases, measured reading comprehension, and a multi-annotator subjective rating of selected text features based on the Hamburg Comprehensibility Concept
Document-level testsuite for evaluation of gender translation consistency.
Our Document-Level test set consists of selected English documents from the WMT21 newstest annotated with gender information. Czech unnanotated references are also added for convenience.
We semi-automatically annotated person names and pronouns to identify the gender of these elements as well as coreferences.
Our proposed annotation consists of three elements: (1) an ID, (2) an element class, and (3) gender.
The ID identifies a person's name and its occurrences (name and pronouns).
The element class identifies whether the tag refers to a name or a pronoun.
Finally, the gender information defines whether the element is masculine or feminine.
We performed a series of NLP techniques to automatically identify person names and coreferences.
This initial process resulted in a set containing 45 documents to be manually annotated.
Thus, we started a manual annotation of these documents to make sure they are correctly tagged.
See README.md for more details.
This data set contains four types of manual annotation of translation quality, focusing on the comparison of human and machine translation quality (aka human-parity). The machine translation system used is English-Czech CUNI Transformer (CUBBITT). The annotations distinguish adequacy, fluency and overall quality. One of the types is Translation Turing test - detecting whether the annotators can distinguish human from machine translation.
All the sentences are taken from the English-Czech test set newstest2018 (WMT2018 News translation shared task www.statmt.org/wmt18/translation-task.html), but only from the half with originally English sentences translated to Czech by a professional agency.
Manual classification of errors of Czech-Slovak translation according to the classification introduced by Vilar et al. [1]. First 50 sentences from WMT 2010 test set were translated by 5 MT systems (Česílko, Česílko2, Google Translate and two Moses setups) and MT errors were manually marked and classified. Classification was applied in MT systems comparison [3]. Reference translation is included.
References:
[1] David Vilar, Jia Xu, Luis Fernando D’Haro and Hermann Ney. Error Analysis of Machine Translation Output. In International Conference on Language Resources and Evaluation, pages 697-702. Genoa, Italy, May 2006.
[2] http://matrix.statmt.org/test_sets/list
[3] Ondřej Bojar, Petra Galuščáková, and Miroslav Týnovský. Evaluating Quality of Machine Translation from Czech to Slovak. In Markéta Lopatková, editor, Information Technologies - Applications and Theory, pages 3-9, September 2011 and This work has been supported by the grants Euro-MatrixPlus (FP7-ICT-2007-3-231720 of the EU and
7E09003 of the Czech Republic)
Manually ranked outputs of Czech-Slovak translations. Three annotators manually ranked outputs of five MT systems (Česílko, Česílko2, Google Translate and two Moses setups) on three data sets (100 sentences randomly selected from books, 100 sentences randomly selected from Acquis corpus and 50 first sentences from WMT 2010 test set). Ranking was applied in MT systems comparison in [1].
References:
[1] Ondřej Bojar, Petra Galuščáková, and Miroslav Týnovský. Evaluating Quality of Machine Translation from Czech to Slovak. In Markéta Lopatková, editor, Information Technologies - Applications and Theory, pages 3-9, September 2011 and This work has been supported by the grant Euro-MatrixPlus (FP7-ICT-2007-3-231720 of the EU and
7E09003 of the Czech Republic)
Mapping table for the article Hajič et al., 2024: Mapping Czech Verbal Valency to PropBank Argument Labels, in LREC-COLING 2024, as preprocess by the algorithm described in the paper. This dataset i smeant for verification (replicatoin) purposes only. It will b manually processed further to arrive at a workable CzezchpropBank, to be used in Czech UMR annotation, to be further updated during the annotation. The resulting PropBank frame files fir Czech are expected to be available with some future releases of UMR, containing Czech UMR annotation, or separately.